Neural networks’ learning process acceleration

This study is devoted to evaluating the process of training of a parallel system in the form of an artificial neural network, which is built using a genetic algorithm. The methods that allow to achieve this goal are computer simulation of a neural network on multi-core CPUs and a genetic algorithm for finding the weights of an artificial neural network. The performance of sequential and parallel training processes of artificial neural network is compared.

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Computer simulation of gas generation and transport in landfills. V: Use of artificial neural network and the genetic algorithm for short- and long-term forecasting and planning

Chemical Engineering Science ◽

10.1016/j.ces.2011.03.013 ◽

2011 ◽

Vol 66 (12) ◽

pp. 2646-2659 ◽

Cited By ~ 18

Author(s):

Hu Li ◽

Raudel Sanchez ◽

S. Joe Qin ◽

Halil I. Kavak ◽

Ian A. Webster ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Computer Simulation ◽

Gas Generation ◽

Short And Long Term ◽

Artificial Neural ◽

Long Term Forecasting

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Aeronautical Study for Flight Time Estimation Applying Artificial Neural Network and Genetic Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.919-921.1063 ◽

2014 ◽

Vol 919-921 ◽

pp. 1063-1074

Author(s):

Yung Ching Lin ◽

Lee Kuo Lin ◽

Shao Hong Tsai

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Traffic Control ◽

Air Traffic Control ◽

Flight Time ◽

Air Traffic ◽

Artificial Neural

Since the adoption of open-air policy, people make more frequent use of air travel to do various business or tourism activities. The volume of air traffic has greatly increased, along with the occurrences of traffic jam in the air. Delays of landings or take-offs and the congestions in the approach air space have become commonplace, exacerbating the already heavy workload of air-traffic controllers and the inadequacies of ATC system. Therefore, a study of flight time in ATC operation to help alleviate airspace congestions has become more and more urgent and important. Taking international airway A1 as an example, this study makes use of the known entry time, flight altitude, speed, penetrating and descending as the input of artificial neural networks; the time between departure and transfer point as the output of Artificial Neural Networks, to establish artificial neural network. Applying artificial neural networks and genetic algorithm to the study to simulate the result of actual flight, one can precisely estimate the flight time, thereby making it an efficient air-traffic-control instrument. It can help controllers handle different time segments of air traffic, thus upgrading the quality of air traffic control service.

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Artificial Neural Network for Assembly Line Balancing

American International Journal of Sciences and Engineering Research ◽

10.46545/aijser.v2i2.121 ◽

2019 ◽

Vol 2 (2) ◽

pp. 69-78

Author(s):

Pius Ucheagwu ◽

Johnmary Ugochukwu Okeke ◽

Christian I. Okonta ◽

Efosa Osamuyimwen

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Neural Networks ◽

Artificial Neural Network ◽

Assembly Line ◽

Assembly Line Balancing ◽

Line Balancing ◽

Simulation Techniques ◽

Artificial Neural

This study examines an assembly line balancing using artificial neural network. An organization that balances the unique workloads must respect the limits and restrictions that hinder the assembly. To optimize the very specific operations, balancing an assembly line may require different methods, including: genetic algorithm, heuristic approach, simulation techniques, the ant colony optimization (ACO), etc., but in this study, artificial neural networks was applied to solving problems of assembly line balancing. This study also explores the characteristics of the assembly line and the classification of the assembly balancing problems, suggesting as an artificial neural network solve.

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AGCS Technique to Improve the Performance of Neural Networks

Journal of Intelligent Systems ◽

10.1515/jisys-2017-0423 ◽

2019 ◽

Vol 29 (1) ◽

pp. 1235-1245

Author(s):

Kishor Kumar Katha ◽

Suresh Pabboju

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Neural Networks ◽

Artificial Neural Network ◽

Cuckoo Search ◽

Overall Performance ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

In The Beginning ◽

Better Than

Abstract In this paper, a fresh method is offered regarding training of particular neural networks. This technique is a combination of the adaptive genetic (AG) and cuckoo search (CS) algorithms, called the AGCS method. The intention of training a particular artificial neural network (ANN) is to obtain the finest weight load. With this protocol, a particular weight is taken into account as feedback, which is optimized by means of the hybrid AGCS protocol. In the beginning, a collection of weights is initialized and the similar miscalculation is discovered. Finally, during training of an ANN, we can easily overcome the training complications involving ANNs and also gain the finest overall performance with training of the ANN. We have implemented the proposed system in MATLAB, and the overall accuracy is about 93%, which is much better than that of the genetic algorithm (86%) and CS (88%) algorithm.

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Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i9.102 ◽

2018 ◽

Vol 6 (9) ◽

pp. 152

Author(s):

S. Vijaya Rani ◽

G. N. K. Suresh Babu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Intrusion Detection ◽

Error Rate ◽

Learning Process ◽

Nearest Neighbor ◽

Detection System ◽

Support Vector ◽

K Nearest Neighbor ◽

Artificial Neural

The illegal hackers penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

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